Correlation and Discovery of Disaster Big Data

Authors: Juanle Wang*, Institute of Geographical Sciences and Natural Resources Research, Kun Bu, International Knowledge Center for Engineering Sciences and Technology under the Auspices of UNESCO, Yuelei Yuan, State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources, Chinese Academy of Sciences, Yujie Wang, State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources, Chinese Academy of Sciences, Xuehua Han, State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources, Chinese Academy of Sciences
Topics: Hazards and Vulnerability
Keywords: Disaster data, disaster risk reduction, data correlation, data discovery, knowledge service
Session Type: Paper
Day: 4/4/2019
Start / End Time: 3:05 PM / 4:45 PM
Room: Roosevelt 5, Marriott, Exhibition Level
Presentation File: No File Uploaded


In recent years, frequent natural disasters have brought huge losses of life and property to the human society and disaster risk reduction has become a common challenge that countries across the globe are facing. Disaster data play a key role in various stages of disaster occurrence and are one of the most important basic supporting conditions for disaster prevention and reduction. With the coming of the Age of Big Data, multi-sourced disaster big data, including monitoring, statistics, investigation, remote sensing, network, etc., provide favorable data sources for disaster risk reduction. However, due to the dispersibility, isomerism, diversity and intersectionality of disaster data, current correlation and discovery of disaster big data is still the bottleneck of disaster data sharing, information analysis and knowledge service. This study proposed a correlation and discovery model of disaster big data and applied the model in practice. First, disaster metadata standards and open expansion principles were formulated. Second, internal and external multi-sourced disaster data resources were accumulated and correlated. Third, extensive disaster resources were integrated, including disaster-related maps, organizations, experts and events, etc. Fourth, disaster knowledge service application was established. Finally, comprehensive application and knowledge discovery capability of disasters were formed and services were provided via the network platform. This research model has been preliminarily applied through the Disaster Risk Reduction Knowledge Service system online (http://drr.ikcest.org). Since the system was online, page views of the platform’s website users have reached 12 thousand per month and about 25% from abroad.

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